|
Abstract:
|
We are developing a novel statistical method to address a fundamental scientific goal: disaggregation, or estimation of the composition of an unknown aggregate target. By combining computer models of the target of interest with measured data, our approach enables computer-model calibration techniques to directly solve the disaggregation problem. We are developing our method in the context of chemical spectra generated by laser-induced breakdown spectroscopy (LIBS), used by instruments such as ChemCam on the Mars Science Laboratory rover Curiosity. Because a single run of the LIBS computer model may take hours on parallel computing platforms, we build fast emulators for targets that consist of a single chemical compound. These single-compound emulators are combined in a Bayesian hierarchical model for multiple-compound (i.e. aggregate) targets. We expect our approach to yield the first statistical characterization of matrix effects, i.e. spectral peaks that are amplified or suppressed when chemical compounds are combined in a target versus measured in isolation, and the first capability in uncertainty quantification that addresses the unique challenges of chemical spectra.
|
